Wavelet Threshold Research Articles

DIAGNOSIS OF ROLLING BEARING DEFECTS BASED ON WAVELET ANALYSIS

Purpose. Development and improvement of a method for analyzing vibration signals from rolling bearings based on wavelet analysis for the detection and identification of equipment defects. Research methods. Wavelet analysis was employed for processing vibration signals from rolling bearings. Threshold wavelet filtering was applied to highlight weak impulse components in the signals, and Morlet wavelet was used to ensure effective filtration. Results. The research results indicate that the proposed method, utilizing wavelet filtering, enhances the speed and reliability of vibration diagnostics for bearings. This allows for the efficient extraction of characteristic frequencies associated with various types of rolling bearing defects. In comparison with other signal analysis methods, the use of the developed method based on continuous wavelet analysis has proven to be particularly effective in extracting characteristic diagnostic frequencies. This method not only allows for the identification of specific types of defects in rolling bearings but also ensures universality, enabling its successful application for analyzing other types of non-stationary signals. Experimental studies have confirmed the high efficiency of the developed method, especially in the early stages of defect development. The application of this method is evident not only in its ability to effectively highlight the characteristic frequencies of bearings but also in its capacity to conduct signal analysis for the identification of equipment defects as a whole. This makes the proposed method a promising and versatile tool for the diagnosis and monitoring of the condition of technical systems. Scientific novelty. Application of the proposed method for processing vibration signals from rolling bearings based on wavelet analysis to improve the effectiveness of defect detection and identification in equipment. Practical value. The developed method can be applied in the industrial sector for the analysis and diagnostics of rolling bearings in equipment. It enables the timely detection of defects, reduces the risk of equipment failure, and lowers operational maintenance costs. Thus, this method has practical value in enhancing the reliability and productivity of industrial equipment.

Development of a Hybrid AI Model for Fault Prediction in Rod Pumping System for Petroleum Well Production

Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste.

Research on noise reduction method of centrifugal pump coupling fault signal based on LMD-FE-IWT

Abstract Aiming at the problem that the coupling fault signal of centrifugal pump presents the complexity of mutual coupling due to the mutual influence of various fault components, and it is easy to be affected by the surrounding environmental noise, which leads to the difficulty of noise reduction, the LMD-FE-IWT noise reduction method is proposed. This method first utilizes local mean decomposition (LMD) to adaptively decompose the vibration signal, and calculates the fuzzy entropy of each component. According to fuzzy entropy, the components are divided into two categories: disordered and ordered. After the improved wavelet threshold denoising of the disordered component, it is reconstructed with the ordered component to achieve the denoising effect. By analying the simulation signal and experimental data, the results show that, the proposed method outperforms other methods, with improvements in signal-to-noise ratio by 55.95%, 48.86%, and 31.71% for the simulation signal noise reduction. The effect is significant, enabling the effective extraction of characteristic frequencies associated with different faults for the experimental signal noise reduction.

Trend Prediction of Vibration Signals for Pumped-Storage Units Based on BA-VMD and LSTM

Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately.

A rolling bearing fault signal denoising algorithm that combines a new adaptive information entropy with a new wavelet threshold function

Abstract Mechanical fault diagnosis is of great significance to industrial automation, and extracting vibration fault signals is one of the important tasks in mechanical health monitoring and fault diagnosis. However, due to the complex working environment of rolling bearings, a large amount of noise makes it difficult to extract vibration fault signals. Denoising the vibration signal of bearings can remove interference noise, simplify the early identification of signal features, and thus improve diagnostic accuracy and mechanical maintenance efficiency. This paper proposes a rolling bearing fault signal denoising algorithm, which constructs a new feature extraction and denoising function. This method first decomposes the noisy signal into Intrinsic Mode Functions (IMFs) by Intrinsic Computing Expressive Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Secondly, a new adaptive information entropy threshold function is constructed to extract the noisy IMFs from it. Then, the noisy IMF signal is denoised by a new wavelet threshold function. Finally, the noise-free IMFs are reconstructed to reconstruct the denoised signal. To verify the actual performance of the algorithm, comparative experiments were conducted on a self-collected dataset and a public dataset, the results show that this method improves the continuity of signal reconstruction and can remove various types of vibration fault noise signals more effectively and accurately, \hl{thereby improving the fault detection accuracy by 2%-9%.

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

When a hydropower unit operates in a sediment-laden river, the sediment accelerates hydro-turbine wear, leading to efficiency loss or even shutdown. Therefore, wear fault diagnosis is crucial for its safe and stable operation. A hydro-turbine wear fault diagnosis method based on improved WT (wavelet threshold algorithm) preprocessing combined with IWSO (improved white shark optimizer) optimized CNN-LSTM (convolutional neural network-long-short term memory) is proposed. The improved WT algorithm is utilized to denoise the preprocessing of the original signals. Chaotic mapping, bird flock search, and cosine elite variation strategies are introduced to enhance the WSO algorithm's robust performance, and the CNN-LSTM model's hyperparameters are optimized using the IWSO algorithm to improve the diagnostic performance. The experimental results show that the accuracy of the proposed method reaches 96.2%, which is 8.9% higher than that of the IWSO-CNN-LSTM model without denoising. The study also found that the diagnostic accuracy of hydro-turbine wear faults increased with increasing sediment concentration in the water. This study can supplement the existing hydro-turbine condition monitoring and fault diagnosis system. Meanwhile, diagnosing wear faults in hydro-turbines can improve power generation efficiency and quality and minimize resource consumption.

Noise reduction method for mine wind speed sensor data based on CEEMDAN-wavelet threshold

The mine wind speed sensor is an important intelligent sensing equipment in the mine intelligent ventilation system that can provide accurate and key wind speed parameters for the intelligent ventilation system. The turbulent pulsation characteristics of the airflow in the underground tunnel are a major factor for the inaccurate measurement of mine wind speed. Therefore, according to the random non-stationary characteristics of a turbulent pulsation signal, a denoising method based on adaptive complete ensemble empirical mode decomposition (CEEMDAN) combined with the wavelet threshold is proposed for suppressing the turbulent pulsation noise in the wind speed signal. First, the CEEMDAN algorithm is used for decomposing the wind speed signal into a series of IMF components. Second, the continuous mean square error criterion is used for determining the high-frequency IMF components with more noise. The wavelet threshold denoising method is used for denoising the high-frequency IMF components with more noise. Finally, the denoised IMF components and remaining low-frequency IMF components are reconstructed for obtaining the denoised signal. The results of the denoising analysis of measured turbulent pulsation signals, comparative analysis of denoising of simulated turbulent pulsation signals by different joint denoising methods, and denoising analysis of actual mine wind speed sensor data indicate that the joint denoising method proposed in this study has a higher signal-to-noise ratio and lower root mean square error of the wind speed signal after denoising. Compared with the EMD-wavelet threshold and EEMD-wavelet threshold denoising methods, the denoising method proposed in this study is better and has higher denoising accuracy, which provides a new method for processing actual mine wind speed sensor data.

Method and Application of Spillway Radial Gate Vibration Signal Denoising on Multiverse Optimization Algorithm-Optimized Variational Mode Decomposition Combined with Wavelet Threshold Denoising

To address the noise issue in the measured vibration signals of spillway radial gate discharge, this paper utilizes the Multiverse Optimization Algorithm (MVO) to optimize the number of decomposition modes (K) and the penalty factor (α) in Variational Mode Decomposition (VMD). This approach ensures improved efficiency of VMD decomposition while maintaining accuracy. Subsequently, the obtained Intrinsic Mode Functions (IMFs) from VMD decomposition are classified based on Multi-scale Permutation Entropy (MPE). IMFs are divided into pure components and noisy components; the noisy components are processed with Wavelet Threshold Denoising (WTD), while the pure components are overlaid and reconstructed to obtain the denoised vibration signal of the gate. Comprehensive comparisons involving artificial signal simulations, gate flow-induced vibration model tests, and numerical simulations lead to the following conclusions: compared to other algorithms, the proposed combined denoising method (MVO-VMD-MPE-WTD) achieves the highest signal-to-noise ratio (SNR) in both the frequency and time domains for artificial signals, while yielding the lowest mean square error (MSE). In the gate flow-induced vibration model tests, the method significantly reduces noise in the vibration signals and effectively preserves characteristic information. The error in preserving characteristic information across model tests and numerical simulations is kept below 1%. Furthermore, compared to other optimization algorithms, the MVO demonstrates higher computational efficiency. The parameter-optimized combined denoising method proposed in this study provides insights into denoising measured vibration signals of hydraulic spillway radial gates and other drainage structures, and it opens possibilities for exploring more efficient optimization algorithms for achieving online monitoring in the future.

Prediction of reference crop evapotranspiration based on improved convolutional neural network (CNN) and long short-term memory network (LSTM) models in Northeast China

The accurate prediction of reference crop evapotranspiration (ET0) is essential to better manage crop irrigation water consumption and improve crop water use efficiency. To effectively improve the accuracy of ET0 simulated by machine learning models, five meteorological stations in Hailaer, Harbin, Hohhot, Changchun, and Dalian were taken as representative stations, daily and monthly ET0 data from 1952 to 2020 were used, and empirical mode decomposition (EMD) and wavelet threshold denoising (WD) were considered. The convolutional neural network (CNN) and long short-term memory network (LSTM) models were improved, and two new hybrid neural network models (EMD–WD–CNN and EMD–WD–LSTM) were constructed. Using the ET0 calculated using the FAO-56 Penman–Monteith (P–M) formula as the standard value, the applicability of the improved machine learning model was evaluated. Results showed the following: i) the daily ET0-PM minimum values of five stations were close to 0, the average values were not significantly increased, and the maximum values significantly fluctuated (the fluctuations in Hailaer and Hohhot showed an upward trend, and the fluctuations in Harbin, Changchun, and Dalian showed a downward trend). The annual average monthly ET0-PM varied seasonally, with the peak in June in the Hailaer station and May in all other stations (the peak in Hohhot was the largest, and the peak in Dalian was the smallest). ii) The daily and monthly ET0 values predicted by the proposed EMD–WD–CNN and EMD–WD–LSTM models were highly consistent with the calculated results of the P–M model, showing high accuracy on the daily and monthly ET0 of the simulated five stations (daily: mean absolute error (MAE), 0.30–0.41 mm/day; root mean square error (RMSE), 0.46–0.60 mm/day; R2, 0.86–0.95; monthly: MAE, 5.66–13.71 mm/month; RMSE, 8.97–18.04 mm/month; R2, 0.91–0.95). iii) The EMD–WD–CNN model was suitable for daily scale ET0 simulation and prediction in Northeast China and monthly scale in Harbin, Changchun, and Hohhot. The EMD–WD–LSTM model was suitable for monthly ET0 simulation and prediction in Hailaer and Dalian in Northeast China. The mixed models of EMD–WD–CNN and EMD–WD–CNN can effectively improve the prediction accuracy of ET0 and can provide a new method for agricultural development and irrigation regulation in Northeast China.

Rolling bearing fault diagnosis method based on SSA-IWT-EMD

Aiming at the problem of insufficient wavelet threshold noise reduction and unclear extraction of characteristic frequency of EMD decomposition, a rolling bearing fault diagnosis method based on SSA-IWT-EMD is proposed. Two adjustment factors are introduced to propose an improved threshold function (IWT), which overcomes the shortcomings of traditional soft and hard thresholds, and the sparrow search optimization algorithm (SSA) is used to globally optimize its parameters to achieve rolling bearing signal noise reduction. A comprehensive index P is proposed to select and reconstruct the components generated by empirical mode decomposition (EMD) to highlight the fault characteristic information of the signal. Envelope spectrum analysis is used to realize bearing fault diagnosis. Simulation and measurement results verify the effectiveness of the proposed method; at the same time, compared with the method of selecting components with a single index and the literature method, it is shown that the comprehensive index P and the proposed method have stronger noise reduction and feature extraction capabilities, and the envelope spectrum amplitude and frequency doubling components are more obvious, which can better realize the fault diagnosis of rolling bearings.

A new combined transient electromagnetic noise reduction method of VMD-SVD-WTD based on improved dung beetle optimization algorithm with multi-strategy fusion

Abstract The transient electromagnetic method (TEM) is now commonly used in the detection of coal mined-out areas, but it is susceptible to various sources of noise during field site probing, significantly impacting the accuracy of detection. Variational mode decomposition (VMD) has gained widespread adoption for eliminating noise in TEM signals. However, a single VMD may not effectively address both full-band noise and residual Gaussian white noise. To address this limitation, the singular value decomposition (SVD) and wavelet threshold denoising (WTD) are introduced correspondingly. Furthermore, this paper tackles challenges related to parameters selection in VMD and traditional optimization algorithms by proposing the integration of dung beetle optimization (DBO) enhanced with circle chaotic mapping, random walk strategy, and crisscross optimization algorithm (CRCDBO). Consequently, a new combined TEM noise reduction method, VMD-SVD-WTD based on CRCDBO is presented. Through comprehensive simulation tests and comparative analyses, the signal-to-noise ratio of this method is improved by 12.79% compared with VMD and the RMSE is 0.0011, which demonstrate the superiority and effectiveness of VMD-SVD-WTD based on CRCDBO in noise reduction. Meanwhile, this method is applied to an iron ore mine in Shanxi, which can accurately monitor the location of the mined-out area and has good application value.

A Study on the Coarse-to-Fine Error Decomposition and Compensation Method of Free-Form Surface Machining

To improve the machining accuracy of free-form surface parts, a coarse-to-fine free-form surface machining error decomposition and compensation method is proposed in this paper. First, the machining error was coarsely decomposed using variational mode decomposition (VMD), and the correlation coefficients between the intrinsic mode function (IMF) and the machining error were obtained to filter out the IMF components that were larger than the thresholding value of the correlation coefficients, which was the coarse systematic error. Second, the coarse systematic errors were finely decomposed using empirical mode decomposition (EMD), which still filters out the IMF components that are larger than the thresholding value of the set correlation coefficient based on the correlation coefficient. Then, the wavelet thresholding method was utilized to finely decompose all the IMF components whose correlation coefficients in the first two decomposition processes were smaller than the threshold value of the correlation coefficient set. The decomposed residual systematic errors were reconstructed with the IMF components screened in the EMD fine decomposition, which gave the fine systematic error. Finally, the machining surface was reconstructed according to the fine systematic error, and its corresponding toolpath was generated to compensate for the machining error without moving the part. The simulation and analysis results of the design show that the method has a more ideal processing error decomposition ability and can decompose the systematic error contained in the processing error in a more complete way. The results of actual machining experiments show that, after using the method proposed in this paper to compensate for the machining error, the maximum absolute machining error decreased from 0.0580 mm to 0.0159 mm, which was a 72.5% reduction, and the average absolute machining error decreased from 0.0472 mm to 0.0059 mm, which was an 87.5% reduction. It was shown that the method was effective and feasible for free-form surface part machining error compensation.

EI-ISOA-VMD: Adaptive denoising and detrending method for nuclear circulating water pump impeller

As a key component of the nuclear circulating water pump, it is difficult to extract effective information from low-quality impeller signal because of the interference noise and non-stationary trend, such as water flow impact, vibrations from unrelated components, and sensor noise. Traditional signal denoising methods, particularly those utilizing variational mode decomposition, often require manual parameter tuning, resulting in unsatisfactory outcome. To address these issues, this paper proposes a signal denoising and detrending method based on the evaluation index driven the improved seagull algorithm optimized for variational mode decomposition (EI-ISOA-VMD). During the optimization process, sinusoidal chaotic mapping is utilized for initializing the seagull population, while a tanh function is employed to design nonlinearly decreasing inertia weights. Furthermore, the Levy flight mechanism enhances the randomness of position updates and optimizes seagull individuals beyond the boundary range in order to form the improved seagull optimization algorithm. The denoising, detrending process, and evaluation index proposed in this paper are utilized as the fitness function for the improved seagull optimization algorithm to optimize key parameters of variational mode decomposition. During the denoising and detrending process, the trend component is initially selected using the mean ratio method. The useful, low noise and high noise intrinsic mode functions are screened by correlation coefficient and weighted permutation entropy. Subsequently, the low noise components are denoised by wavelet thresholding method, and the high noise components are discarded to finally reconstruct the signal. The experimental results demonstrate that the proposed method effectively eliminates noise and trend components in low-quality signal while preserving more valuable information.

Electrical Tree Image De-Noising using Threshold Wavelet Transform and Wiener Filter

Electrical treeing occurred in solid dielectric materials, especially in electrical application with high voltage. The occurrence of electrical tree happens when high electric fields applied, causing tiny channels or paths to form. The main issue during the data collection process is the changes of lighting, making it difficult to study the tree's propagation length, fractal dimension, and growth rate due to corrupted images. This research aims to analyse electrical tree structure images in XLPE material using a CCD camera and develop image de-noising techniques to suppress noise on the electrical tree image. The performance was then analysed using the Otsu thresholding algorithm for accurate segmentation. The methodology was divided into four phases: sample preparation, experimental setup, image pre-processing in MATLAB, and testing four de-noising filters: Wiener, median, NLM, and Gaussian. The Wiener filter with higher PSNR, SNR, and RMSE was selected and using superimposed method, both threshold wavelet transforms and wiener was combined to eliminate the noise. Finally, the proposed method of superimposed was tested with the Otsu thresholding method to evaluate accuracy, sensitivity, and specificity of the combination filter. Based on the analysis of PSNR, SNR, and RMSE, the performance of the threshold wavelet and Wiener filter (TWWF) de-noising technique improves the image quality of the electrical tree structure. Thus, for the Otsu thresholding segmentation algorithm analysis, it also had the highest values in terms of accuracy, sensitivity, and specificity.

Research on Pedestrian and Cyclist Classification Method Based on Micro-Doppler Effect.

In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver's field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification.

Denoising method for colonic pressure signals based on improved wavelet threshold.

The colonic peristaltic pressure signal is helpful for the diagnosis of intestinal diseases, but it is difficult to reflect the real situation of colonic peristalsis due to the interference of various factors. To solve this problem, an improved wavelet threshold denoising method based on discrete wavelet transform is proposed in this paper. This algorithm can effectively extract colonic peristaltic pressure signals and filter out noise. Firstly, a threshold function with three shape adjustment factors is constructed to give the function continuity and better flexibility. Then, a threshold calculation method based on different decomposition levels is designed. By adjusting the three preset shape factors, an appropriate threshold function is determined, and denoising of colonic pressure signals is achieved through hierarchical thresholding. In addition, the experimental analysis of bumps signal verifies that the proposed denoising method has good reliability and stability when dealing with non-stationary signals. Finally, the denoising performance of the proposed method was validated using colonic pressure signals. The experimental results indicate that, compared to other methods, this approach performs better in denoising and extracting colonic peristaltic pressure signals, aiding in further identification and treatment of colonic peristalsis disorders.

Research on trajectory tracking control method for agricultural robot permanent magnet synchronous motor servo system based on improved EMD threshold wavelet filtering

In agricultural robots, trajectory tracking can be affected by disturbances such as road slopes or bumps, leading to sudden changes in path curvature and reduced control accuracy. To address this issue, we propose a servo motor drive control method based on an improved Empirical Mode Decomposition (EMD) threshold. A mathematical model integrating the drive and control of the servo motor is established, and the driving performance of the servo motor is analyzed. By detecting the speed and position of the servo motor, the specified three-phase current values for motor drive control are derived. The actual current of the motor is collected using Hall current sensors and denoised with an improved EMD threshold wavelet filtering method. Within the servo motor drive control model, the system calculates the difference between the given three-phase current values and the actual motor current, and this difference is then used to adjust the motor control via an input linear amplifier, ensuring integrated drive control. Simulation and experimental results show that the proposed trajectory tracking control method for the agricultural robot’s permanent magnet synchronous motor servo system exhibits high control accuracy, strong anti-interference ability, and good performance, making it more suitable for practical applications.

Deep Learning-Based Nonparametric Identification and Path Planning for Autonomous Underwater Vehicles

As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to select the optimal threshold and wavelet basis functions, and the raw model test data are denoising. Second, the bidirectional temporal convolutional networks, the bidirectional gated recurrent unit, and the attention mechanism are used to achieve the nonlinear nonparametric model of the AUV motion. And the hyperparameters are optimized by the DBO. Finally, the lazy-search-based path planning and the line-of-sight-based path following control are used for the proposed AUV model. The simulation shows that the prediction accuracy of the DBO-DTCN is better than other artificial intelligence methods and mechanical models, and the path following of AUV is feasible. The methods proposed in this paper can provide an effective strategy for AUV modeling, searching, and rescue cruising.

Research on Signal Noise Reduction and Leakage Localization in Urban Water Supply Pipelines Based on Northern Goshawk Optimization.

In order to enhance the accuracy and adaptability of urban water supply pipeline leak localization, based on the Northern Goshawk Optimization, a novel joint denoising method is proposed in this paper to reduce noise in negative pressure wave signals caused by leaks. Firstly, the Northern Goshawk Optimization optimizes the decomposition levels and penalty factors of Variational Mode Decomposition, and obtains their optimal combination. Subsequently, the optimized parameters are used to decompose the pressure signals into modal components, and the effective components and noise components are distinguished according to the correlation coefficients. Then, an optimized wavelet thresholding method is applied to the selected effective components for secondary denoising. Finally, the signal components that have been denoised twice are reconstructed with the effective signal components, and the denoised negative pressure wave signals are obtained. Simulation experiments demonstrate that compared to wavelet transforms and Empirical Mode Decomposition, our method achieves the highest signal-to-noise ratio improvement of 12.23 dB and normalized cross correlation of 0.991. It effectively preserves useful leak information in the signal while suppressing noise, laying a solid foundation for improving leak localization accuracy. After several leak simulation tests on the leakage simulation test platform, the test results verify the effectiveness of the proposed method. The minimum relative error of the leakage localization is 0.29%, and an average relative error is 1.64%, achieving accurate leakage localization.

A Review of Image Denoising Using Fuzzy and Wiener Filters in the Wavelet Domain

This paper focuses on image denoising using fuzzy wavelet domain transforms, reviewing recent advancements in this area. Wavelet transforms have become a powerful tool in image denoising, with one of the most widely used techniques involving thresholding wavelet coefficients. The paper proposes a hybrid denoising method that combines the wavelet transform, median filtering, and nonlinear diffusion. Additionally, a novel fuzzy filter is introduced to reduce additive noise in digital color images. Two distinct image denoising techniques are discussed: the first employs an Asymmetrical Triangular Moving Average Filter (TMAV) with a HAAR wavelet transform, while the second utilizes an Asymmetrical Triangular Median Filter (TMED) with the HAAR wavelet transform.